Page 48 - CCFA Journal - Second Issue

加中金融

(2)智能预警监控体系 B. Intelligent early warning monitoring system
Comprehensive information on commercial, financial, sector,
通过整合多个来源的数据，获得工商、财务、行业、司法、 legal, public opinion, and more is obtained by integrating data
舆情等多个维度的全面信息。由系统自动分析预警，从而 from multiple sources. The system automatically analyzes the
提高贷后资产管理的工作效率，并降低人工操作风险。此 early warning, which improves the efficiency of post-credit
外，跟传统监控模式相比，除了信息源比较全面，而且可 asset management and reduces the risk of manual operation.
以对监控对象的全产业链监控。根据产业链上下游的价格、 Further, compared with the traditional monitoring mode, not
订单等波动情况预测企业的风险。 only the information source is more comprehensive, but also
can monitor the entire industry chain.
利用数字化科技可实现监控的信息集成化、行为自动化、 Using digital technology, we can realize the information
频率实时化、结果可视化，有效提升监控的智能化水平。 integration, behavior automation, frequency real-time, results
在客户层面，可利用信息科技强化数据收集分析能力、计 visualization, and effectively improve the intelligent level of
算建模能力、非结构化数据处理能力，及时挖掘外部数据 monitoring.
中蕴含的风险信息，全方位地对授信客户作出实时、全面
的风险预测。在组合层面，可利用信息科技构建覆盖行业、 C. Intelligent credit rating system
区域、客户、产品的多维度、多时点信用监控体系，提升 Artificial intelligence can build a credit analysis model by
组合监控能力。在人员方面，可利用信息科技监控业务人 means of technology and data. ML algorithms, such as gradient
员在业务流程各环节的操作行为，防范违规操作。 improvement decision tree, random forest, neural network,
cluster adjustment technology, incremental learning technology,
etc., can make objective credit analysis and evaluation for
people who lack traditional credit records.
(3)智能信用评级体系 A new generation of customer rating systems based on big data
and ML has many advantages over the traditional ratings. One
人工智能通过技术、数据的手段可以构建出一个信用分析 is that algorithms are more efficient. Using machine learning
模型。通过梯度提升决策树、随机森林、神经网络、分群 algorithm, the study of law is more accurate and the processing
调整技术、增量学习技术等在内的机器学习算法，可以为 of information is more efficient. The second is faster iterations.
缺少传统信贷记录的人群做出客观的信用分析评价。 A new version of the model can be automatically developed
based on the latest data re-learning rules to adapt to the
基于大数据、机器学习的新一代客户评级系统，相对于传 changing risk laws of the customer. Third, the characteristic
统评级具有诸多优势。一是算法更有效。采用机器学习算 indicators are richer and the dimensions are more novel. The
法，对规律的学习更准确，处理信息的效率更高。二是迭 system from the original model of 10 to 20 indicators expanded
代更快速。可根据最新数据重新学习规则，自动形成新版 to more than 400 indicators, not only have a wealth of financial
模型，以适应客户不断变化的风险规律。三是特征指标更 indicators and non-financial objective indicators, but also can
丰富、维度更新颖。系统由原先一个模型 10～20 个指标 tap many innovative dimension indicators, such as based on
扩展至 400 多个指标，不仅有更丰富的财务指标与非财务 customer funds trading network information, customer
客观指标，还可挖掘诸多创新维度指标，如基于客户资金 knowledge map information. Fourth, ratings are less volatile
交易网络信息、客户知识图谱的信息等。四是评级波动性 and more anti-jamming. The system is not susceptible to the
更小、抗干扰能力更强。系统不易受单一指标的影响造成 influence of a single indicator caused by large fluctuations in
评级大幅波动，对部分指标的错误录入能够做到相对少干 ratings, the error entry of some indicators can achieve relatively
扰评级结果。 little interference with the rating results.
D. intelligent macro-policy analysis.

(4)智能宏观政策分析 Enterprises can set up a policy industry information platform to
support the latest policy push and retrieval queries, and use
企业可建立政策行业信息平台，支持最新政策推送及检索 natural language processing technology to achieve the industry,
查询，并利用自然语言处理技术，实现行业、地区、热点 region, hot areas of policy focus policy map, to assist business
领域政策的焦点政策图谱，协助业务人员第一时间获取行 personnel to obtain industry management dynamics in the first
业管理动态，提高政策的分析效率。 place, improve policy analysis efficiency.

CCFA JOURNAL OF FINANCE DECEMBER 2020
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